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Inter-fraction deformable image registration using unsupervised deep learning for CBCT-guided abdominal radiotherapy.

Huiqiao Xie1, Yang Lei1, Yabo Fu1,2

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States of America.

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Summary

This study introduces an unsupervised deep learning method for Cone-Beam CT (CBCT) registration in radiotherapy. The approach accurately quantifies anatomical changes during treatment, improving image-guided radiotherapy analysis.

Keywords:
cbctdeep learningdeformable image registrationradiotherapy

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Cone-Beam CT (CBCT) is vital in image-guided radiotherapy for patient setup and plan evaluation.
  • Longitudinal CBCT registration tracks inter-fractional anatomical changes like tumor shrinkage and organ variations.
  • Accurate registration is crucial for quantitative analysis of treatment-induced anatomical alterations.

Purpose of the Study:

  • To propose an unsupervised deep learning-based CBCT-CBCT deformable image registration method.
  • To enable quantitative analysis of inter-fractional anatomical variations during radiotherapy.
  • To develop a method for fast and accurate longitudinal CBCT alignment.

Main Methods:

  • A spatial transformation-based network (STN) using GlobalGAN and LocalGAN for coarse and fine motion prediction.
  • Unsupervised training minimizing image similarity and deformable vector field (DVF) regularization loss.
  • Inference stage fusing local and global DVF predictions for whole-image registration.

Main Results:

  • Qualitative assessment showed good alignment between deformed and target CBCT images.
  • Quantitative evaluation yielded an average target registration error of 1.91 ± 1.18 mm.
  • Performance metrics included average mean absolute error of 33.42 ± 7.48 HU and normalized cross-correlation of 0.94 ± 0.04.

Conclusions:

  • An unsupervised deep learning CBCT-CBCT registration method was successfully developed and validated.
  • The method demonstrates feasibility and strong performance for fractionated image-guided radiotherapy.
  • This approach facilitates accurate longitudinal CBCT alignment for inter-fractional anatomical change analysis and prediction.